Catherine Nakalembe

So that's one part of it.

Sometimes we think about what satellite data and AI, et cetera, can do in agriculture.

I think a lot of people think about it the same way as how chat GPT enables you to write an email.

You can immediately check that the text is wrong.

You can correct it, right?

But it's gotten information from a lot of text that's been digitized.

Agriculture has not been digitized.

There's not a lot of text from which my model will learn from to give me the right sentence.

So in the context for my sister, who has some coffee here, some maize here, I think there was cacao too.

Her field is not represented in existing data.

So I could try to map it really well.

I could get all the information directly on the ground.

But I'd still do a pretty bad job because the model has learned about other things that have nothing to do with our reality.

So there's one side of it.

One side of it is in order to get useful information from satellite data, we build models.

You have to train the model using some existing example.

And then the model goes through all the satellite data and tries to find that thing that you were looking for.

And that's how we would create like a crop type map.

Today, most models do very well for farmland in the United States.

The fields are really big.